A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Incremental sparse saliency detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Optimal contrast based saliency detection
Pattern Recognition Letters
Ensemble dictionary learning for saliency detection
Image and Vision Computing
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The guidance of attention helps the human vision system (HVS) to detect and recognize objects rapidly. In this paper, we propose a bottom-up saliency algorithm based on sparse coding theory. Sparse coding decomposes the inputs into two parts, codes and residual. From the viewpoint of biological vision and information theory, the coding length is closely related to the local complexity while the residual is closely related to the uncertainty. The proposed algorithm defines the weighted residual using sparse coding length as saliency. By multiplying the L0 norm of sparse codes and the residual, a saliency map is obtained. The performance of the proposed method is evaluated using ROC curves with two different scale datasets and is compared with state-of-the-art models. Our algorithm outperforms all other methods and the results indicate a robust and accurate saliency.